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Results: 47
Number of items: 47
  • Open Access
    Ariannezhad, M., Yahya, M., Meij, E., Schelter, S., & de Rijke, M. (2022). Understanding Financial Information Seeking Behavior from User Interactions with Company Filings. In WWW '22 Companion: companion proceedings of the Web Conference 2022: April 25, 2022, Lyon, France (pp. 586-594). Association for Computing Machinery. https://doi.org/10.1145/3487553.3524636
  • Open Access
    Schelter, S. (2022). Letter from the Special Issue Editor. Bulletin of the Technical Committee on Data Engineering, 45(1), 2-3. http://sites.computer.org/debull/A22mar/p2.pdf
  • Open Access
    Grafberger, S., Groth, P., Stoyanovich, J., & Schelter, S. (2022). Data distribution debugging in machine learning pipelines. VLDB Journal, 31(5), 1103-1126. https://doi.org/10.1007/s00778-021-00726-w
  • Sprangers, O., Schelter, S., & de Rijke, M. (2021). Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression. In KDD ’21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining : August 14-18, 2021, virtual event, Singapore (pp. 1510-1520). Association for Computing Machinery. https://doi.org/10.1145/3447548.3467278
  • Ariannezhad, M., Jullien, S., Nauts, P., Fang, M., Schelter, S., & de Rijke, M. (2021). Understanding Multi-Channel Customer Behavior in Retail. In CIKM '21: proceedings of the 30th ACM International Conference on Information & Knowledge Management : November 1-5, 2021, virtual event, Australia (pp. 2867–2871). The Association for Computing Machinery. https://doi.org/10.1145/3459637.3482208
  • Open Access
    Kersbergen, B., & Schelter, S. (2021). Learnings from a Retail Recommendation System on Billions of Interactions at bol.com. In 2021 IEEE 37th International Conference on Data Engineering: ICDE 2021 : proceedings : Chania, Greece, 19-22 April 2021 (pp. 2447-2452). (International Conference on Data Engineering; Vol. 37). IEEE Computer Society. https://doi.org/10.1109/ICDE51399.2021.00277
  • Open Access
    Schelter, S., Grafberger, S., & Dunning, T. (2021). HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning. In SIGMOD '21: proceedings of the 2021 International Conference on the Management of Data : June 20 -25, 2021, virtual event, China (pp. 1545–1557). Association for Computing Machinery. https://doi.org/10.1145/3448016.3457239
  • Open Access
    Grafberger, S., Guha, S., Stoyanovich, J., & Schelter, S. (2021). MLINSPECT: A Data Distribution Debugger for Machine Learning Pipelines. In SIGMOD '21: proceedings of the 2021 International Conference on the Management of Data : June 20 -25, 2021, virtual event, China (pp. 2736–2739). Association for Computing Machinery. https://doi.org/10.1145/3448016.3452759
  • Open Access
    Schelter, S. (2021). Letter from the Special Issue Editor. Bulletin of the Technical Committee on Data Engineering, 44(1), 2. http://sites.computer.org/debull/A21mar/p2.pdf
  • Schelter, S., Rukat, T., & Biessmann, F. (2020). Learning to Validate the Predictions of Black Box Classifiers on Unseen Data. In SIGMOD '20: proceedings of the 2020 ACM SIGMOD International Conference on Management of Data : June 14-19, 2020, Portland, OR, USA (pp. 1289-1299). Association for Computing Machinery. https://doi.org/10.1145/3318464.3380604
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